Abstract

Many binary code encoding schemes based on hashing
have been actively studied recently, since they can provide efficient similarity search, especially nearest neighbor
search, and compact data representations suitable for handling large scale image databases in many computer vision problems.
Existing hashing techniques encode high dimensional data points by using hyperplane-based hashing functions. In this paper we propose a novel hypersphere-
based hashing function, spherical hashing, to map more spatially coherent data points into a binary code compared
to hyperplane-based hashing functions. Furthermore, we propose a new binary code distance function, spherical
Hamming distance, that is tailored to our hypersphere-based binary coding scheme, and design an efficient iterative
optimization process to achieve balanced partitioning of data points for each hash function and independence between hashing functions. Our extensive experiments show
that our spherical hashing technique significantly outperforms six state-of-the-art hashing techniques based on hyperplanes across various image benchmarks
of sizes ranging from one to 75 million of GIST descriptors. The performance gains are consistent and large, up to 100% improvements.
The excellent results confirm the unique merits of the proposed idea in using hyperspheres to encode proximity regions in high-dimensional spaces.
Finally, our method is intuitive and easy to implement.